ClassCut for Unsupervised Class Segmentation

Abstract. We propose a novel method for unsupervised class segmentation on a set of images. It alternates between segmenting object instances and learning a class model. The method is based on a segmentation energy defined over all images at the same time, which can be optimized efficiently by techniques used before in interactive segmentation. Over iterations, our method progressively learns a class model by integrating observations over all images. In addition to appearance, this model captures the location and shape of the class with respect to an automatically determined coordinate frame common across images. This frame allows us to build stronger shape and location models, similar to those used in object class detection. Our method is inspired by interactive segmentation methods [1], but it is fully automatic and learns models characteristic for the object class rather than specific to one particular object/image. We experimentally demonstrate on the Caltech4, Caltech101, and Weizmann horses datasets that our method (a) transfers class knowledge across images and this improves results compared to segmenting every image independently; (b) outperforms Grabcut [1] for the task of unsupervised segmentation; (c) offers competitive performance compared to the state-of-the-art in unsupervised segmentation and in particular it outperforms the topic model [2].

The impact of asking intention or self-prediction questions on subsequent behavior: a meta-analysis

The current meta-analysis estimated the magnitude of the impact of asking intention and self-prediction questions on rates of subsequent behavior, and examined mediators and moderators of this question–behavior effect (QBE). Random-effects meta-analysis on 116 published tests of the effect indicated that intention/prediction questions have a small positive effect on behavior (d+ = 0.24). Little support was observed for attitude accessibility, cognitive dissonance, behavioral simulation, or processing fluency explanations of the QBE. Multivariate analyses indicated significant effects of social desirability of behavior/behavior domain (larger effects for more desirable and less risky behaviors), difficulty of behavior (larger effects for easy-to-perform behaviors), and sample type (larger effects among student samples). Although this review controls for co-occurrence of moderators in multivariate analyses, future primary research should systematically vary moderators in fully factorial designs. Further primary research is also needed to unravel the mechanisms underlying different variants of the QBE

Does observability affect prosociality?

The observation of behaviour is a key theoretical parameter underlying a number of models of prosociality. However, the empirical findings showing the effect of observability on prosociality are mixed. In this meta-analysis, we explore the boundary conditions that may account for this variability, by exploring key theoretical and methodological moderators of this link. We identified 117 papers yielding 134 study level effects (Total N = 788, 164) and found a small but statistically significant, positive association between observability and prosociality (r = .141, 95% CI = .106, .175). Moderator analysis showed that observability produced stronger effects on prosociality (1) in the presence of passive observers (i.e., people whose role was to only observe participants) vs perceptions of being watched, (2) when participants decisions were consequential (vs non-consequential), (3) when the studies were performed in the laboratory (as opposed to in the field/online), (4) when studies used repeated measures (instead of single games) and (5) when studies involved social dilemmas (instead of bargaining games). These effects show the conditions under which observability effects on prosociality will be maximally observed. We describe the theoretical and practical significance of 14 these results

Introducing EMMIE: An evidence rating scale to encourage mixed-method crime prevention synthesis reviews

Objectives This short report describes the need for, and the development of, a coding system to distil the quality and coverage of systematic reviews of the evidence relating to crime prevention interventions. The starting point for the coding system concerns the evidence needs of policymakers and practitioners. Methods The coding scheme (EMMIE) proposed builds on previous scales that have been developed to assess the probity, coverage and utility of evidence both in health and criminal justice. It also draws on the principles of realist synthesis and review. Results The proposed EMMIE scale identifies five dimensions to which systematic reviews intended to inform crime prevention should speak. These are the Effect of intervention, the identification of the causal Mechanism(s) through which interventions are intended to work, the factors that Moderate their impact, the articulation of practical Implementation issues, and the Economic costs of intervention

A visual category filter for Google images

We extend the constellation model to include heterogeneous parts which may represent either the appearance or the geometry of a region of the object. The pans and their spatial configuration are learnt simultaneously and automatically, without supervision, from cluttered images. We describe how this model can be employed for ranking the output of an image search engine when searching for object categories. It is shown that visual consistencies in the output images can be identified, and then used to rank the images according to their closeness to the visual object category. Although the proportion of good images may be small, the algorithm is designed to be robust and is capable of learning in either a totally unsupervised manner, or with a very limited amount of supervision. We demonstrate the method on image sets returned by Google's image search for a number of object categories including bottles, camels, cars, horses, tigers and zebras

Citrulline as a marker of intestinal function and absorption in clinical settings: A systematic review and meta-analysis

Background: Citrulline has been described as a marker of intestinal function or absorption but evidence varies according to clinical settings. Objective: The objective of this article is to examine the evidence of plasma citrulline as a marker of intestinal function and absorption in various clinical settings. Methods: Studies were examined for p values, means and standard deviations, correlation coefficients or other metrics depicting the association of citrulline with intestinal function. A random effects model was used to produce a pooled estimate. A hierarchical summary receiver operating curve model was fitted for diagnostic accuracy measures. Results: Citrulline levels are correlated strongly with small bowel length in short bowel syndrome patients (r = 0.67). Citrulline is strongly negatively correlated (r = –0.56) with intestinal disease severity with regards to enteropathies (coeliac disease, tropical enteropathy, Crohn’s disease, mucositis, acute rejection in intestinal transplantation). Citrulline cut-off levels have an overall sensitivity and specificity of 80% and 84% respectively. Citrulline levels in untreated coeliac patients compared to controls were reduced by 10 µmol/l. Citrulline levels increase with gluten-free diet and with improvement of enteropathy. Citrulline is decreased in critical illness and sepsis. Conclusion: These findings allow us to advocate quite reasonably that citrulline is a marker of acute and chronic intestinal insufficiency